@article{Ho_Su_Li_Bolger_Pan_2020, title={Machine Vision and Deep Learning Based Rubber Gasket Defect Detection}, volume={5}, url={https://ojs.imeti.org/index.php/AITI/article/view/4278}, DOI={10.46604/aiti.2020.4278}, abstractNote={<p>This study develops an automated optical inspection system for silicone rubber gaskets using traditional rule-based and deep learning detection techniques. The specific object of interest is a 5 mm × 10 mm × 5 mm  mobile device power supply connector gasket that provides protection against foreign body inclusion and water ingression. The proposed system can detect a total of five characteristic defects introduced during the mold-based manufacture process, which range from 10-100 μm. The deep learning detection strategies in this system employ convolutional neural networks (CNN) developed using the TensorFlow open-source library. Through both high dynamic range image capture and image generation techniques, accuracies of 100% and 97% are achieved for notch and residual glue defect predictions, respectively.</p>}, number={2}, journal={Advances in Technology Innovation}, author={Ho, Chao-Ching and Su, Eugene and Li, Po-Chieh and Bolger, Matthew J. and Pan, Huan-Ning}, year={2020}, month={Apr.}, pages={76–83} }